Triple
T34415692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinn |
E883397
|
entity |
| Predicate | hasEnglishContrastTerm |
P193435
|
FINISHED |
| Object | reference |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: reference | Statement: [Sinn, hasEnglishContrastTerm, reference]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEnglishContrastTerm Context triple: [Sinn, hasEnglishContrastTerm, reference]
-
A.
hasEnglishGloss
Indicates that one entity serves as the English-language gloss or explanatory translation for the other entity.
-
B.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
C.
hasEnglishNameVariant
Indicates that one entity is an alternative or variant form of another entity’s name specifically in the English language.
-
D.
hasEnglishComponent
Indicates that something includes or is associated with a component that is in the English language.
-
E.
equivalentEnglishForm
Indicates that two expressions share the same meaning in English, serving as equivalent linguistic forms.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f349c2e3b88190a67834eb5bcffeaf |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
| PDg | Predicate description generation | batch_69fd474a71648190b6b6ae4991db81b1 |
completed | May 8, 2026, 2:15 a.m. |
Created at: May 1, 2026, 1:59 a.m.